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J Proteome Res. 2018 Dec 7;17(12):4307-4314. doi: 10.1021/acs.jproteome.8b00422. Epub 2018 Oct 5.

Sequential Fractionation Strategy Identifies Three Missing Proteins in the Mitochondrial Proteome of Commonly Used Cell Lines.

Author information

1
Department of Medical, Oral and Biotechnological Sciences , University G. D'Annunzio Chieti , Chieti-Pescara, Via dei Vestini 31 , 66100 Chieti , Italy.
2
Proteomics and Metabonomics Unit , IRCCS Fondazione Santa Lucia , Via del Fosso di Fiorano 64 , 00143 Rome , Italy.
3
Institute of Biochemistry and Clinical Biochemistry , Università Cattolica del Sacro Cuore , L.go F. Vito 1 , 00168 Rome , Italy.
4
Department of Laboratory Diagnostic and Infectious Diseases , Fondazione Policlinico Universitario Agostino Gemelli-IRCCS , L.go A. Gemelli 8 , 00168 Rome , Italy.
5
Department of Chemical Sciences , University of Catania , V.le A. Doria 6 , 95125 Catania , Italy.

Abstract

Mitochondria are undeniably the cell powerhouse, directly affecting cell survival and fate. Growing evidence suggest that mitochondrial protein repertoire affects metabolic activity and plays an important role in determining cell proliferation/differentiation or quiescence shift. Consequently, the bioenergetic status of a cell is associated with the quality and abundance of the mitochondrial populations and proteomes. Mitochondrial morphology changes in the development of different cellular functions associated with metabolic switches. It is therefore reasonable to speculate that different cell lines do contain different mitochondrial-associated proteins, and the investigation of these pools may well represent a source for mining missing proteins (MPs). A very effective approach to increase the number of IDs through mass spectrometry consists of reducing the complexity of the biological samples by fractionation. The present study aims at investigating the mitochondrial proteome of five phenotypically different cell lines, possibly expressing some of the MPs, through an enrichment-fractionation approach at the organelle and protein level. We demonstrate a substantial increase in the proteome coverage, which, in turn, increases the likelihood of detecting low abundant proteins, often falling in the category of MPs, and resulting, for the present study, in the identification of METTL12, FAM163A, and RGS13. All MS data have been deposited to the MassIVE data repository ( https://massive.ucsd.edu ) with the data set identifier MSV000082409 and PXD010446.

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